code: def get_mma_fill_intrin(dtype, local_size):
      zero = IntImm("int32", 0).astype(dtype)
  
      # Assume M = N = 16
      index_map = shared_16x16_to_ldmatrix_32x8_layout
  
      @T.prim_func
      def mma_fill_desc(a: T.handle) -> None:
          C_warp = T.match_buffer(
              a, [WARP_SIZE, local_size], dtype=dtype, scope="warp"
          )
  
          with T.block("root"):
              T.reads()
              T.writes(C_warp[0:WARP_SIZE, 0:local_size])
              for i0, i1 in T.grid(M_DIM, N_DIM):
                  with T.block("C_warp"):
                      i, j = T.axis.remap("SS", [i0, i1])
                      thread_id, local_id = T.meta_var(index_map(i, j))
                      T.reads()
                      T.writes(C_warp[thread_id, local_id])
                      C_warp[thread_id, local_id] = zero
  
      @T.prim_func
      def mma_fill_impl(a: T.handle) -> None:
          C_warp = T.match_buffer(
              a,
              [WARP_SIZE, local_size],
              dtype=dtype,
              scope="warp",
              offset_factor=1,
          )
  
          with T.block("root"):
              T.reads()
              T.writes(C_warp[0:WARP_SIZE, 0:local_size])
  
              for tx in T.thread_binding(0, WARP_SIZE, "threadIdx.x"):
                  T.evaluate(
                      T.mma_fill(
                          local_size,
                          C_warp.data,
                          C_warp.elem_offset,
                          dtype=dtype,
                      )
                  )
  
      return mma_fill_desc, mma_fill_impl
  
imports: from typing import Dict, Optional, Tuple, Literal
  from tvm._ffi import register_func
  from tvm.runtime import convert
  from tvm.script import tir
  from tvm.tir.function import PrimFunc
  from tvm.tir import Cast, IntImm, TensorIntrin
label_desc: Use image node_id 8 for calling a global function with example usage: get_mma_fill_intrin(dtype, local_size) and returns: mma_fill_desc,  mma_fill_impl